Enterprise software systems (enterprise systems) can be used to perform enterprise-level operations. Example enterprise systems can include enterprise resource planning (ERP) systems, client-relationship management (CRM) systems, product lifecycle management (PLM) systems, supply chain management (SCM) systems, and supplier relationship management (SRM) systems. In a production environment, numerous users interact with an enterprise system daily, resulting in hundreds, if not thousands of transactions, and relatively large amounts of data. In some instances, enterprise systems, can be updated and/or customized. For example, a more recent version of an enterprise system may be available, resulting in an update to the enterprise system (e.g., updating from version 1.0 to version 2.0). As another example, an enterprise can customize an enterprise system to include functionality/features that are relevant to the particular enterprise.
In cases of updating and/or customizing (modifying) of an enterprise system the modified enterprise system is tested before being made available for production use. In some examples, testing is performed using production data (e.g., data provided from production use of the enterprise system before updating/customizing). Traditionally, shadow testing of the modified enterprise system is achieved by copying the production system (or tenant, if the software is tenant enabled) to an empty system (or tenant) and begin testing with semi-production data. This, however, has several disadvantages.
For example, the setup of such a test system requires copying of the entire production system, which might consume significant memory (e.g., up to several terabytes (TB)). Further, the copy-procedure may require a significant amount of time (e.g., a few hours to several days). During this time, the production use of the source system would create inconsistent data within the copy. Consequently, production use of the system is either halted, leading to significant downtime, or the testing is performed using inconsistent data. A further disadvantage is that, until the new system is performing exactly as the old system, the data will diverge and produce errors that make it difficult to identify the root cause of the issue. Additionally, traditional shadow testing requires that all transactions run in the origin system also get run in the test system, making targeted testing difficult.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.